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Instruction 13.6 One of the Most Common Questions of Prospective House Buyers

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Instruction 13.6
One of the most common questions of prospective house buyers pertains to the average cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Celsius (X1) , the amount of insulation in cm (X2) , the number of windows in the house (X3) and the age of the furnace in years (X4) . Given below are the Microsoft Excel outputs of two regression models.
Instruction 13.6 One of the most common questions of prospective house buyers pertains to the average cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Celsius (X<sub>1</sub>) , the amount of insulation in cm (X<sub>2</sub>) , the number of windows in the house (X<sub>3</sub>) and the age of the furnace in years (X<sub>4</sub>) . Given below are the Microsoft Excel outputs of two regression models. -Referring to Instruction 13.6,the estimated value of the partial regression parameter β<sub>1</sub> in Model 1 means that A) holding the effect of the other independent variables constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B) holding the effect of the other independent variables constant, a 1% increase in the daily minimum outside temperature results in an estimated expected decrease in heating costs by 4.51%. C) holding the effect of the other independent variables constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. D) holding the effect of the other independent variables constant, a 1 degree increase in the daily minimum outside temperature results in an estimated expected decrease in heating costs by $4.51. Instruction 13.6 One of the most common questions of prospective house buyers pertains to the average cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Celsius (X<sub>1</sub>) , the amount of insulation in cm (X<sub>2</sub>) , the number of windows in the house (X<sub>3</sub>) and the age of the furnace in years (X<sub>4</sub>) . Given below are the Microsoft Excel outputs of two regression models. -Referring to Instruction 13.6,the estimated value of the partial regression parameter β<sub>1</sub> in Model 1 means that A) holding the effect of the other independent variables constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B) holding the effect of the other independent variables constant, a 1% increase in the daily minimum outside temperature results in an estimated expected decrease in heating costs by 4.51%. C) holding the effect of the other independent variables constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. D) holding the effect of the other independent variables constant, a 1 degree increase in the daily minimum outside temperature results in an estimated expected decrease in heating costs by $4.51.
-Referring to Instruction 13.6,the estimated value of the partial regression parameter β1 in Model 1 means that

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